




'''simple docstring'''


import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
  import onnxruntime as ort





@nightly
@require_onnxruntime
@require_torch_gpu
class  __lowercase    (	unittest.TestCase			):







   @property
   def 		UpperCamelCase__      (						self   )					->     str:
     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )







   @property
   def 		UpperCamelCase__      (						self   )					->     Dict:
     __a								=       ort.SessionOptions()
     __a								=       False
     return options







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo.png'   )
     __a								=       load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'   )
     __a								=       load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'   )

     # using the PNDM scheduler by default
     __a								=       OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
         'CompVis/stable-diffusion-v1-4'   ,				revision='onnx'   ,				safety_checker=UpperCamelCase   ,				feature_extractor=UpperCamelCase   ,				provider=self.gpu_provider   ,				sess_options=self.gpu_options   ,				)
     pipe.set_progress_bar_config(disable=UpperCamelCase   )

     __a								=       'A red cat sitting on a park bench'

     __a								=       np.random.RandomState(0   )
     __a								=       pipe(
         prompt=UpperCamelCase   ,				image=UpperCamelCase   ,				mask_image=UpperCamelCase   ,				strength=0.75   ,				guidance_scale=7.5   ,				num_inference_steps=15   ,				generator=UpperCamelCase   ,				output_type='np'   ,				)

     __a								=       output.images[0]

     assert image.shape == (512, 512, 3)
     assert np.abs(expected_image - image   ).max() < 1e-2









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_					=   {
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
      "ClapModel",
      "ClapPreTrainedModel",
      "ClapTextModel",
      "ClapTextModelWithProjection",
      "ClapAudioModel",
      "ClapAudioModelWithProjection",
  ]
  UpperCAmelCase_					=   ["ClapFeatureExtractor"]

if TYPE_CHECKING:
  from .configuration_clap import (
      CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
      ClapAudioConfig,
      ClapConfig,
      ClapTextConfig,
  )
  from .processing_clap import ClapProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_clap import ClapFeatureExtractor
    from .modeling_clap import (
        CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
        ClapAudioModel,
        ClapAudioModelWithProjection,
        ClapModel,
        ClapPreTrainedModel,
        ClapTextModel,
        ClapTextModelWithProjection,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



